Perhaps the most significant development on the Internet in recent years has been the rapid proliferation of online social networks, such as LinkedIn® and Facebook®. Billions of users are presently accessing such social networks to connect with friends and acquaintances and to share personal and professional information. During operation, these online social networks routinely make millions of decisions each day to determine which types of information to present to users. Some of these decisions can directly affect the revenue of the online social network. In particular, online social networks routinely make decisions to determine which types of subscription offers to present to users. For example, a more-expensive subscription may allow a user to perform more-sophisticated searches through member records to look for sales leads, or may make it easier for a user to contact other members of the online social network.
However, designing a system to make good decisions about subscription offers can be a challenging task because a user's preferences and associated behavior can change over time. Hence, it is desirable for the decision-making methodology to be able to adapt to these changing preferences and behaviors.